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Yi Li
Researcher at Lancaster University
Publications - 12
Citations - 1771
Yi Li is an academic researcher from Lancaster University. The author has contributed to research in topics: Battery (electricity) & Lithium-ion battery. The author has an hindex of 7, co-authored 11 publications receiving 833 citations. Previous affiliations of Yi Li include Laborelec & Vrije Universiteit Brussel.
Papers
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Journal ArticleDOI
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Yi Li,Yi Li,Yi Li,Kailong Liu,Aoife Foley,Alana Aragon Zulke,Alana Aragon Zulke,Maitane Berecibar,Elise Nanini-Maury,Joeri Van Mierlo,Harry E. Hoster,Harry E. Hoster +11 more
TL;DR: This review categorises data-driven battery health estimation methods according to their underlying models/algorithms and discusses their advantages and limitations, then focuses on challenges of real-time battery health management and discuss potential next-generation techniques.
Journal ArticleDOI
A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter
Yi Li,Yi Li,Mohamed Abdel-Monem,Mohamed Abdel-Monem,Rahul Gopalakrishnan,Maitane Berecibar,Elise Nanini-Maury,Noshin Omar,Peter Van Den Bossche,Joeri Van Mierlo +9 more
TL;DR: In this paper, an advanced state of health (SoH) estimation method for high energy NMC lithium-ion batteries based on the incremental capacity (IC) analysis is proposed.
Journal ArticleDOI
Random forest regression for online capacity estimation of lithium-ion batteries
Yi Li,Changfu Zou,Maitane Berecibar,Elise Nanini-Maury,Jonathan Cheung-Wai Chan,Peter Van Den Bossche,Joeri Van Mierlo,Noshin Omar +7 more
TL;DR: The proposed machine-learning technique, random forest regression, is able to learn the dependency of the battery capacity on the features that are extracted from the charging voltage and capacity measurements, and is promising for online battery capacity estimation.
Journal ArticleDOI
Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries
TL;DR: This is the first-known data-driven application that utilizes the GPR with ARD kernel to perform battery calendar aging prognosis and shows good generalization ability and accurate prediction results for calendar aging under various storage conditions.
Journal ArticleDOI
Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries
TL;DR: Li et al. as mentioned in this paper developed a machine-learning-enabled data-driven models for effective capacity predictions for lithium-ion (Li-ion) batteries under different cyclic conditions, which is able to achieve satisfactory results for both one-step and multistep predictions.